ArticlePloS one2022
Estimating the basic reproduction number at the beginning of an outbreak.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of epidemiological modelling in response to lumpy skin disease outbreaks.Frontiers in veterinary science · 2024Pooled it
- Dynamical behavior analysis of 2-control strategies on tuberculosis model.PLOS global public health · 2026Article
- Modeling and Characterizing the Growth of the Texas-New Mexico Measles Outbreak of 2025.Epidemiologia (Basel, Switzerland) · 2025Article
- Rnaught: a web tool for estimating the basic reproduction number.BMC global and public health · 2025Article
- A Retrospective Study of the Effects of COVID-19 Non-Pharmaceutical Interventions on Influenza in Canada.Infectious disease reports · 2025Article
- Article
- Comparative Dynamics of Delta and Omicron SARS-CoV-2 Variants across and between California and Mexico.Viruses · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We compare several popular methods of estimating the basic reproduction number, R0, focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R0 estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.